# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask
# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
%matplotlib inline
warnings.filterwarnings("ignore")
nltk.download('all')
[nltk_data] Downloading collection 'all' [nltk_data] | [nltk_data] | Downloading package abc to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package abc is already up-to-date! [nltk_data] | Downloading package alpino to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package alpino is already up-to-date! [nltk_data] | Downloading package biocreative_ppi to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package biocreative_ppi is already up-to-date! [nltk_data] | Downloading package brown to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package brown is already up-to-date! [nltk_data] | Downloading package brown_tei to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package brown_tei is already up-to-date! [nltk_data] | Downloading package cess_cat to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package cess_cat is already up-to-date! 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[nltk_data] | [nltk_data] Done downloading collection all
True
# path = '/content/drive/MyDrive/Files/'
path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
df_tvshows = pd.read_csv(path + 'otttvshows.csv')
df_tvshows.head()
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Snowpiercer | 2013 | 18+ | 6.9 | 94% | NaN | Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso... | Action,Drama,Sci-Fi,Thriller | United States | English | Set seven years after the world has become a f... | 60.0 | tv series | 3.0 | 1 | 0 | 0 | 0 | 1 |
| 1 | 2 | Philadelphia | 1993 | 13+ | 8.8 | 80% | NaN | Charlie Day,Glenn Howerton,Rob McElhenney,Kait... | Comedy | United States | English | The gang, 5 raging alcoholic, narcissists run ... | 22.0 | tv series | 18.0 | 1 | 0 | 0 | 0 | 1 |
| 2 | 3 | Roma | 2018 | 18+ | 8.7 | 93% | NaN | Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ... | Action,Drama,History,Romance,War | United Kingdom,United States | English | In this British historical drama, the turbulen... | 52.0 | tv series | 2.0 | 1 | 0 | 0 | 0 | 1 |
| 3 | 4 | Amy | 2015 | 18+ | 7.0 | 87% | NaN | Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma... | Drama | United States | English | A family drama focused on three generations of... | 60.0 | tv series | 6.0 | 1 | 0 | 1 | 1 | 1 |
| 4 | 5 | The Young Offenders | 2016 | NaN | 8.0 | 100% | NaN | Alex Murphy,Chris Walley,Hilary Rose,Dominic M... | Comedy | United Kingdom,Ireland | English | NaN | 30.0 | tv series | 3.0 | 1 | 0 | 0 | 0 | 1 |
# profile = ProfileReport(df_tvshows)
# profile
def data_investigate(df):
print('No of Rows : ', df.shape[0])
print('No of Coloums : ', df.shape[1])
print('**'*25)
print('Colums Names : \n', df.columns)
print('**'*25)
print('Datatype of Columns : \n', df.dtypes)
print('**'*25)
print('Missing Values : ')
c = df.isnull().sum()
c = c[c > 0]
print(c)
print('**'*25)
print('Missing vaules %age wise :\n')
print((100*(df.isnull().sum()/len(df.index))))
print('**'*25)
print('Pictorial Representation : ')
plt.figure(figsize = (10, 10))
sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
plt.show()
data_investigate(df_tvshows)
No of Rows : 5432
No of Coloums : 20
**************************************************
Colums Names :
Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
dtype='object')
**************************************************
Datatype of Columns :
ID int64
Title object
Year int64
Age object
IMDb float64
Rotten Tomatoes object
Directors object
Cast object
Genres object
Country object
Language object
Plotline object
Runtime float64
Kind object
Seasons float64
Netflix int64
Hulu int64
Prime Video int64
Disney+ int64
Type int64
dtype: object
**************************************************
Missing Values :
Age 1954
IMDb 556
Rotten Tomatoes 4194
Directors 5158
Cast 486
Genres 323
Country 549
Language 638
Plotline 2493
Runtime 1410
Seasons 679
dtype: int64
**************************************************
Missing vaules %age wise :
ID 0.000000
Title 0.000000
Year 0.000000
Age 35.972018
IMDb 10.235641
Rotten Tomatoes 77.209131
Directors 94.955817
Cast 8.946981
Genres 5.946244
Country 10.106775
Language 11.745214
Plotline 45.894698
Runtime 25.957290
Kind 0.000000
Seasons 12.500000
Netflix 0.000000
Hulu 0.000000
Prime Video 0.000000
Disney+ 0.000000
Type 0.000000
dtype: float64
**************************************************
Pictorial Representation :
# ID
# df_tvshows = df_tvshows.drop(['ID'], axis = 1)
# Age
df_tvshows.loc[df_tvshows['Age'].isnull() & df_tvshows['Disney+'] == 1, "Age"] = '13'
# df_tvshows.fillna({'Age' : 18}, inplace = True)
df_tvshows.fillna({'Age' : 'NR'}, inplace = True)
df_tvshows['Age'].replace({'all': '0'}, inplace = True)
df_tvshows['Age'].replace({'7+': '7'}, inplace = True)
df_tvshows['Age'].replace({'13+': '13'}, inplace = True)
df_tvshows['Age'].replace({'16+': '16'}, inplace = True)
df_tvshows['Age'].replace({'18+': '18'}, inplace = True)
# df_tvshows['Age'] = df_tvshows['Age'].astype(int)
# IMDb
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].mean()}, inplace = True)
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].median()}, inplace = True)
df_tvshows.fillna({'IMDb' : "NA"}, inplace = True)
# Rotten Tomatoes
df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].astype(int)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].mean()}, inplace = True)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].median()}, inplace = True)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'].astype(int)
df_tvshows.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
# Directors
# df_tvshows = df_tvshows.drop(['Directors'], axis = 1)
df_tvshows.fillna({'Directors' : "NA"}, inplace = True)
# Cast
df_tvshows.fillna({'Cast' : "NA"}, inplace = True)
# Genres
df_tvshows.fillna({'Genres': "NA"}, inplace = True)
# Country
df_tvshows.fillna({'Country': "NA"}, inplace = True)
# Language
df_tvshows.fillna({'Language': "NA"}, inplace = True)
# Plotline
df_tvshows.fillna({'Plotline': "NA"}, inplace = True)
# Runtime
# df_tvshows.fillna({'Runtime' : df_tvshows['Runtime'].mean()}, inplace = True)
# df_tvshows['Runtime'] = df_tvshows['Runtime'].astype(int)
df_tvshows.fillna({'Runtime' : "NA"}, inplace = True)
# Kind
# df_tvshows.fillna({'Kind': "NA"}, inplace = True)
# Type
# df_tvshows.fillna({'Type': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Type'], axis = 1)
# Seasons
# df_tvshows.fillna({'Seasons': 1}, inplace = True)
df_tvshows.fillna({'Seasons': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Seasons'], axis = 1)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
# df_tvshows.fillna({'Seasons' : df_tvshows['Seasons'].mean()}, inplace = True)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
# Service Provider
df_tvshows['Service Provider'] = df_tvshows.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_tvshows.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)
# Removing Duplicate and Missing Entries
df_tvshows.dropna(how = 'any', inplace = True)
df_tvshows.drop_duplicates(inplace = True)
data_investigate(df_tvshows)
No of Rows : 5432
No of Coloums : 21
**************************************************
Colums Names :
Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
'Service Provider'],
dtype='object')
**************************************************
Datatype of Columns :
ID int64
Title object
Year int64
Age object
IMDb object
Rotten Tomatoes object
Directors object
Cast object
Genres object
Country object
Language object
Plotline object
Runtime object
Kind object
Seasons object
Netflix int64
Hulu int64
Prime Video int64
Disney+ int64
Type int64
Service Provider object
dtype: object
**************************************************
Missing Values :
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :
ID 0.0
Title 0.0
Year 0.0
Age 0.0
IMDb 0.0
Rotten Tomatoes 0.0
Directors 0.0
Cast 0.0
Genres 0.0
Country 0.0
Language 0.0
Plotline 0.0
Runtime 0.0
Kind 0.0
Seasons 0.0
Netflix 0.0
Hulu 0.0
Prime Video 0.0
Disney+ 0.0
Type 0.0
Service Provider 0.0
dtype: float64
**************************************************
Pictorial Representation :
df_tvshows.head()
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Snowpiercer | 2013 | 18 | 6.9 | 94 | NA | Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso... | Action,Drama,Sci-Fi,Thriller | United States | ... | Set seven years after the world has become a f... | 60 | tv series | 3 | 1 | 0 | 0 | 0 | 1 | Netflix |
| 1 | 2 | Philadelphia | 1993 | 13 | 8.8 | 80 | NA | Charlie Day,Glenn Howerton,Rob McElhenney,Kait... | Comedy | United States | ... | The gang, 5 raging alcoholic, narcissists run ... | 22 | tv series | 18 | 1 | 0 | 0 | 0 | 1 | Netflix |
| 2 | 3 | Roma | 2018 | 18 | 8.7 | 93 | NA | Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ... | Action,Drama,History,Romance,War | United Kingdom,United States | ... | In this British historical drama, the turbulen... | 52 | tv series | 2 | 1 | 0 | 0 | 0 | 1 | Netflix |
| 3 | 4 | Amy | 2015 | 18 | 7 | 87 | NA | Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma... | Drama | United States | ... | A family drama focused on three generations of... | 60 | tv series | 6 | 1 | 0 | 1 | 1 | 1 | Netflix |
| 4 | 5 | The Young Offenders | 2016 | NR | 8 | 100 | NA | Alex Murphy,Chris Walley,Hilary Rose,Dominic M... | Comedy | United Kingdom,Ireland | ... | NA | 30 | tv series | 3 | 1 | 0 | 0 | 0 | 1 | Netflix |
5 rows × 21 columns
df_tvshows.describe()
| ID | Year | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|
| count | 5432.000000 | 5432.000000 | 5432.000000 | 5432.000000 | 5432.000000 | 5432.000000 | 5432.0 |
| mean | 2716.500000 | 2010.668446 | 0.341311 | 0.293999 | 0.403351 | 0.033689 | 1.0 |
| std | 1568.227662 | 11.726176 | 0.474193 | 0.455633 | 0.490615 | 0.180445 | 0.0 |
| min | 1.000000 | 1901.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.0 |
| 25% | 1358.750000 | 2009.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.0 |
| 50% | 2716.500000 | 2014.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.0 |
| 75% | 4074.250000 | 2017.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 1.0 |
| max | 5432.000000 | 2020.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0 |
df_tvshows.corr()
| ID | Year | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|
| ID | 1.000000 | -0.031346 | -0.646330 | 0.034293 | 0.441264 | 0.195409 | NaN |
| Year | -0.031346 | 1.000000 | 0.222316 | -0.065807 | -0.198675 | -0.022741 | NaN |
| Netflix | -0.646330 | 0.222316 | 1.000000 | -0.366515 | -0.515086 | -0.119344 | NaN |
| Hulu | 0.034293 | -0.065807 | -0.366515 | 1.000000 | -0.377374 | -0.075701 | NaN |
| Prime Video | 0.441264 | -0.198675 | -0.515086 | -0.377374 | 1.000000 | -0.151442 | NaN |
| Disney+ | 0.195409 | -0.022741 | -0.119344 | -0.075701 | -0.151442 | 1.000000 | NaN |
| Type | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
# df_tvshows.sort_values('Year', ascending = True)
# df_tvshows.sort_values('IMDb', ascending = False)
# df_tvshows.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_otttvshows.csv', index = False)
# path = '/content/drive/MyDrive/Files/'
# udf_tvshows = pd.read_csv(path + 'updated_otttvshows.csv')
# udf_tvshows
# df_netflix_tvshows = df_tvshows.loc[(df_tvshows['Netflix'] > 0)]
# df_hulu_tvshows = df_tvshows.loc[(df_tvshows['Hulu'] > 0)]
# df_prime_video_tvshows = df_tvshows.loc[(df_tvshows['Prime Video'] > 0)]
# df_disney_tvshows = df_tvshows.loc[(df_tvshows['Disney+'] > 0)]
df_netflix_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 1) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_hulu_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 1) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_prime_video_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 1 ) & (df_tvshows['Disney+'] == 0)]
df_disney_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 1)]
df_tvshows_age = df_tvshows.copy()
df_tvshows_age.drop(df_tvshows_age.loc[df_tvshows_age['Age'] == "NA"].index, inplace = True)
df_tvshows_age.drop(df_tvshows_age.loc[df_tvshows_age['Age'] == "NR"].index, inplace = True)
# df_tvshows_age = df_tvshows_age[df_tvshows_age.Age != "NA"]
df_tvshows_age['Age'] = df_tvshows_age['Age'].astype(int)
# Creating distinct dataframes only with the tvshows present on individual streaming platforms
netflix_age_tvshows = df_tvshows_age.loc[df_tvshows_age['Netflix'] == 1]
hulu_age_tvshows = df_tvshows_age.loc[df_tvshows_age['Hulu'] == 1]
prime_video_age_tvshows = df_tvshows_age.loc[df_tvshows_age['Prime Video'] == 1]
disney_age_tvshows = df_tvshows_age.loc[df_tvshows_age['Disney+'] == 1]
df_tvshows_age_group = df_tvshows_age.copy()
plt.figure(figsize = (10, 10))
corr = df_tvshows_age.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, allow annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()
<Figure size 720x720 with 0 Axes>
df_age_all_tvshows = df_tvshows_age
print('\nTV Shows with Age Rating are : \n')
df_age_all_tvshows.head(5)
TV Shows with Age Rating are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Snowpiercer | 2013 | 18 | 6.9 | 94 | NA | Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso... | Action,Drama,Sci-Fi,Thriller | United States | ... | Set seven years after the world has become a f... | 60 | tv series | 3 | 1 | 0 | 0 | 0 | 1 | Netflix |
| 1 | 2 | Philadelphia | 1993 | 13 | 8.8 | 80 | NA | Charlie Day,Glenn Howerton,Rob McElhenney,Kait... | Comedy | United States | ... | The gang, 5 raging alcoholic, narcissists run ... | 22 | tv series | 18 | 1 | 0 | 0 | 0 | 1 | Netflix |
| 2 | 3 | Roma | 2018 | 18 | 8.7 | 93 | NA | Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ... | Action,Drama,History,Romance,War | United Kingdom,United States | ... | In this British historical drama, the turbulen... | 52 | tv series | 2 | 1 | 0 | 0 | 0 | 1 | Netflix |
| 3 | 4 | Amy | 2015 | 18 | 7 | 87 | NA | Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma... | Drama | United States | ... | A family drama focused on three generations of... | 60 | tv series | 6 | 1 | 0 | 1 | 1 | 1 | Netflix |
| 8 | 9 | Quincy | 2018 | 18 | 7.3 | 82 | NA | Jack Klugman,John S. Ragin,Robert Ito,Joseph R... | Crime,Drama,Mystery,Thriller | United States | ... | Quincy and Sam are working as Coroners. Inspec... | 60 | tv series | 8 | 1 | 0 | 0 | 0 | 1 | Netflix |
5 rows × 21 columns
df_age_0_tvshows = df_tvshows_age.loc[df_tvshows_age['Age'] == 0]
print('\nTV Shows with All Age Rating are : \n')
df_age_0_tvshows.head(5)
TV Shows with All Age Rating are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 24 | 25 | Kung Fu Panda Holiday | 2010 | 0 | 6.8 | NA | Tim Johnson | Jack Black,Dustin Hoffman,Angelina Jolie,Seth ... | Animation,Short,Action,Comedy,Family | United States | ... | The Winter Festival is coming and Po is asked ... | 21 | tv series | NA | 1 | 0 | 0 | 0 | 1 | Netflix |
| 29 | 30 | Zapped | 2014 | 0 | 6.8 | 6 | Peter DeLuise | James Buckley,Kenneth Collard,Louis Emerick,Pa... | Comedy,Fantasy | United Kingdom | ... | NA | 30 | tv series | 3 | 1 | 1 | 0 | 1 | 1 | Netflix |
| 62 | 63 | True: Happy Hearts Day | 2019 | 0 | 8.3 | NA | Harold Harris | Michela Luci,Jamie Watson,Eric Peterson,Anna C... | Animation,Short,Adventure,Family,Fantasy | NA | ... | NA | NA | tv series | NA | 1 | 0 | 0 | 0 | 1 | Netflix |
| 81 | 82 | Beat Bugs: All Together Now | 2017 | 0 | 7 | NA | Josh Wakely,Pablo De La Torre | Ashleigh Ball,Lili Beaudoin,Shannon Chan-Kent,... | Animation,Short,Adventure,Comedy,Family,Fantas... | United States | ... | NA | NA | tv series | NA | 1 | 0 | 0 | 0 | 1 | Netflix |
| 103 | 104 | Amazing Grace | 2018 | 0 | 7.1 | 67 | Michael Apted | Kate Jenkinson,Sigrid Thornton,Alex Dimitriade... | Drama | Australia | ... | The series centres on midwife Grace and her pa... | 118 | tv series | 1 | 0 | 1 | 0 | 0 | 1 | Hulu |
5 rows × 21 columns
df_age_7_tvshows = df_tvshows_age.loc[df_tvshows_age['Age'] == 7]
print('\nTV Shows with 7+ Age Rating are : \n')
df_age_7_tvshows.head(5)
TV Shows with 7+ Age Rating are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 27 | 28 | The Adventures of Sharkboy and Lavagirl | 2005 | 7 | 8 | 19 | Doug Walker | Doug Walker,Malcolm Ray,Tamara Chambers,Barney... | Comedy,Talk-Show | NA | ... | If you thought the Spy Kids sequels were bad..... | 29 | tv series | NA | 1 | 0 | 0 | 0 | 1 | Netflix |
| 45 | 46 | LEGO Jurassic World: The Indominus Escape | 2016 | 7 | 5.7 | NA | NA | A.J. LoCascio,Sendhil Ramamurthy,Fred Tatascio... | Animation,Action,Adventure,Comedy,Family,Sci-Fi | United States | ... | Jurassic park founder, Simon Masrani, recruits... | 24 | tv series | 1 | 1 | 0 | 0 | 0 | 1 | Netflix |
| 76 | 77 | Pac’s Scary Halloween | 2016 | 7 | 5.4 | NA | NA | Ashleigh Ball,Gabriel C. Brown,Ian James Corle... | Animation,Action,Comedy,Drama,Family,Fantasy,S... | United States | ... | NA | 44 | tv series | NA | 1 | 0 | 0 | 0 | 1 | Netflix |
| 88 | 89 | EMI | 2008 | 7 | 4.3 | NA | NA | Carmindy,Ted Gibson,Clinton Kelly,Stacy London | Family,Reality-TV | NA | ... | NA | 44 | tv series | NA | 1 | 0 | 0 | 0 | 1 | Netflix |
| 99 | 100 | Jake's Buccaneer Blast | 2014 | 7 | 5.2 | NA | NA | Megan Richie,Jadon Sand,Riley Thomas Stewart,D... | Animation | United States | ... | NA | NA | tv series | 1 | 1 | 0 | 0 | 0 | 1 | Netflix |
5 rows × 21 columns
df_age_13_tvshows = df_tvshows_age.loc[df_tvshows_age['Age'] == 13]
print('\nTV Shows with 13+ Age Rating are : \n')
df_age_13_tvshows.head(5)
TV Shows with 13+ Age Rating are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | Philadelphia | 1993 | 13 | 8.8 | 80 | NA | Charlie Day,Glenn Howerton,Rob McElhenney,Kait... | Comedy | United States | ... | The gang, 5 raging alcoholic, narcissists run ... | 22 | tv series | 18 | 1 | 0 | 0 | 0 | 1 | Netflix |
| 30 | 31 | Yol Arkadaşım 2 | 2018 | 13 | 6.4 | NA | NA | Bigkem Melisa Özelçi,Asena Keskinci,Serhat Mus... | Drama,Romance | Turkey | ... | NA | NA | tv series | 2 | 1 | 0 | 0 | 0 | 1 | Netflix |
| 55 | 56 | The Road to El Camino: Behind the Scenes of El... | 2019 | 13 | 7.1 | NA | NA | Charles Baker,Jonathan Banks,Melissa Bernstein... | Documentary,Short | United States | ... | NA | 13 | tv series | NA | 1 | 0 | 0 | 0 | 1 | Netflix |
| 82 | 83 | The Birth Reborn 3 | 2018 | 13 | NA | NA | NA | Michel Odent | Documentary | NA | ... | NA | NA | tv series | NA | 1 | 0 | 0 | 0 | 1 | Netflix |
| 102 | 103 | Get Smart | 2008 | 13 | 8.2 | 51 | Peter Segal | Don Adams,Barbara Feldon,Edward Platt,Robert K... | Action,Adventure,Comedy,Crime,Family,Mystery,S... | United States | ... | Maxwell Smart is a bumbling secret agent, assi... | 25 | tv series | 5 | 0 | 1 | 0 | 0 | 1 | Hulu |
5 rows × 21 columns
df_age_16_tvshows = df_tvshows_age.loc[df_tvshows_age['Age'] == 16]
print('\nTV Shows with 16+ Age Rating are : \n')
df_age_16_tvshows.head(5)
TV Shows with 16+ Age Rating are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 54 | 55 | My True Friend | 2012 | 16 | 7.7 | NA | Atsajun Sattakovit | Angelababy,Allen Deng,Yilong Zhu,Kai Tan,Anlia... | Drama,Romance | China | ... | A story that revolves around real estate agent... | 103 | tv series | 1 | 1 | 0 | 1 | 0 | 1 | Netflix |
| 147 | 148 | Dark Money | 2018 | 16 | 6.6 | 96 | NA | Babou Ceesay,Jill Halfpenny,Susan Wokoma,Olive... | Crime,Drama,Thriller | United Kingdom | ... | Life is not always like the movies. Love isn't... | 220 | tv series | 1 | 0 | 0 | 1 | 0 | 1 | Prime Video |
| 157 | 158 | Halo 4: Forward Unto Dawn | 2012 | 16 | 6.9 | NA | NA | Thom Green,Anna Popplewell,Enisha Brewster,Aye... | Action,Adventure,Family,Sci-Fi,Thriller,War | United States | ... | In real life, Robert Oppenheimer was the scien... | 100 | tv series | 1 | 0 | 0 | 1 | 0 | 1 | Prime Video |
| 161 | 162 | Innocent | 2011 | 16 | 5.4 | 64 | NA | Mario Casas,Juana Acosta,Josean Bengoetxea,Oli... | Crime,Drama,Mystery,Thriller | Spain | ... | Legendary poet, singer/songwriter and alleged ... | 45 | tv series | 1 | 0 | 0 | 1 | 0 | 1 | Prime Video |
| 178 | 179 | Houdini | 2014 | 16 | 7.4 | NA | NA | Adrien Brody,Kristen Connolly,Evan Jones,Tim P... | Biography,Drama | United States,Canada | ... | At the beginning of a nightly Alcoholics Anony... | 174 | tv series | 1 | 0 | 0 | 1 | 0 | 1 | Prime Video |
5 rows × 21 columns
df_age_18_tvshows = df_tvshows_age.loc[df_tvshows_age['Age'] == 18]
print('\nTV Shows with 18+ Age Rating are : \n')
df_age_18_tvshows.head(5)
TV Shows with 18+ Age Rating are :
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Snowpiercer | 2013 | 18 | 6.9 | 94 | NA | Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso... | Action,Drama,Sci-Fi,Thriller | United States | ... | Set seven years after the world has become a f... | 60 | tv series | 3 | 1 | 0 | 0 | 0 | 1 | Netflix |
| 2 | 3 | Roma | 2018 | 18 | 8.7 | 93 | NA | Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ... | Action,Drama,History,Romance,War | United Kingdom,United States | ... | In this British historical drama, the turbulen... | 52 | tv series | 2 | 1 | 0 | 0 | 0 | 1 | Netflix |
| 3 | 4 | Amy | 2015 | 18 | 7 | 87 | NA | Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma... | Drama | United States | ... | A family drama focused on three generations of... | 60 | tv series | 6 | 1 | 0 | 1 | 1 | 1 | Netflix |
| 8 | 9 | Quincy | 2018 | 18 | 7.3 | 82 | NA | Jack Klugman,John S. Ragin,Robert Ito,Joseph R... | Crime,Drama,Mystery,Thriller | United States | ... | Quincy and Sam are working as Coroners. Inspec... | 60 | tv series | 8 | 1 | 0 | 0 | 0 | 1 | Netflix |
| 11 | 12 | Wakefield | 2017 | 18 | 8.1 | 72 | Robin Swicord | Rudi Dharmalingam,Mandy McElhinney,Geraldine H... | Mystery | Australia | ... | NA | 106 | tv series | 1 | 1 | 0 | 0 | 0 | 1 | Netflix |
5 rows × 21 columns
f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(df_tvshows_age['Age'],bins = 20, kde = True, ax = ax[0])
sns.boxplot(df_tvshows_age['Age'], ax = ax[1])
plt.show()
# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Age s Per Platform')
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_age_tvshows['Age'][:100], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_age_tvshows['Age'][:100], color = 'red', legend = True, kde = True)
sns.histplot(hulu_age_tvshows['Age'][:100], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_age_tvshows['Age'][:100], color = 'darkblue', legend = True, kde = True)
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()
def round_val(data):
if str(data) != 'nan':
return round(data)
df_tvshows_age_group['Age Group'] = df_tvshows_age['Age'].apply(round_val)
age_values = df_tvshows_age_group['Age Group'].value_counts().sort_index(ascending = False).tolist()
age_index = df_tvshows_age_group['Age Group'].value_counts().sort_index(ascending = False).index
# age_values, age_index
age_group_count = df_tvshows_age_group.groupby('Age Group')['Title'].count()
age_group_tvshows = df_tvshows_age_group.groupby('Age Group')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
age_group_data_tvshows = pd.concat([age_group_count, age_group_tvshows], axis = 1).reset_index().rename(columns = {'Title' : 'TV Shows Count'})
age_group_data_tvshows = age_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)
# Age Group with TV Shows Counts - All Platforms Combined
age_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)
| Age Group | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 3 | 16 | 1018 | 350 | 521 | 236 | 7 |
| 1 | 7 | 958 | 334 | 372 | 306 | 72 |
| 4 | 18 | 946 | 497 | 233 | 253 | 1 |
| 0 | 0 | 521 | 147 | 150 | 207 | 73 |
| 2 | 13 | 64 | 4 | 9 | 26 | 30 |
age_group_data_tvshows.sort_values(by = 'Age Group', ascending = False)
| Age Group | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 4 | 18 | 946 | 497 | 233 | 253 | 1 |
| 3 | 16 | 1018 | 350 | 521 | 236 | 7 |
| 2 | 13 | 64 | 4 | 9 | 26 | 30 |
| 1 | 7 | 958 | 334 | 372 | 306 | 72 |
| 0 | 0 | 521 | 147 | 150 | 207 | 73 |
fig = px.bar(y = age_group_data_tvshows['TV Shows Count'],
x = age_group_data_tvshows['Age Group'],
color = age_group_data_tvshows['Age Group'],
color_continuous_scale = 'Teal_r',
labels = { 'y' : 'TV Shows Count', 'x' : 'Age : '},
title = 'TV Shows with Group Age : All Platforms')
fig.update_layout(plot_bgcolor = "white")
fig.show()
fig = px.pie(age_group_data_tvshows,
names = age_group_data_tvshows['Age Group'],
values = age_group_data_tvshows['TV Shows Count'],
color = age_group_data_tvshows['TV Shows Count'],
color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textinfo = 'percent+label',
title = 'TV Shows Count based on Age Group')
fig.show()
df_age_group_high_tvshows = age_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False).reset_index()
df_age_group_high_tvshows = df_age_group_high_tvshows.drop(['index'], axis = 1)
# filter = (age_group_data_tvshows['TV Shows Count'] == (age_group_data_tvshows['TV Shows Count'].max()))
# df_age_group_high_tvshows = age_group_data_tvshows[filter]
# highest_rated_tvshows = age_group_data_tvshows.loc[age_group_data_tvshows['TV Shows Count'].idxmax()]
# print('\nAge with Highest Ever TV Shows Count are : All Platforms Combined\n')
df_age_group_high_tvshows.head(5)
| Age Group | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 16 | 1018 | 350 | 521 | 236 | 7 |
| 1 | 7 | 958 | 334 | 372 | 306 | 72 |
| 2 | 18 | 946 | 497 | 233 | 253 | 1 |
| 3 | 0 | 521 | 147 | 150 | 207 | 73 |
| 4 | 13 | 64 | 4 | 9 | 26 | 30 |
df_age_group_low_tvshows = age_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = True).reset_index()
df_age_group_low_tvshows = df_age_group_low_tvshows.drop(['index'], axis = 1)
# filter = (age_group_data_tvshows['TV Shows Count'] = = (age_group_data_tvshows['TV Shows Count'].min()))
# df_age_group_low_tvshows = age_group_data_tvshows[filter]
# print('\nAge with Lowest Ever TV Shows Count are : All Platforms Combined\n')
df_age_group_low_tvshows.head(5)
| Age Group | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 13 | 64 | 4 | 9 | 26 | 30 |
| 1 | 0 | 521 | 147 | 150 | 207 | 73 |
| 2 | 18 | 946 | 497 | 233 | 253 | 1 |
| 3 | 7 | 958 | 334 | 372 | 306 | 72 |
| 4 | 16 | 1018 | 350 | 521 | 236 | 7 |
print(f'''
Total '{df_tvshows_age['Age'].count()}' Titles are available on All Platforms, out of which\n
You Can Choose to see TV Shows from Total '{age_group_data_tvshows['Age Group'].unique().shape[0]}' Age Group, They were Like this, \n
{age_group_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)['Age Group'].unique()} etc. \n
The Age Group with Highest TV Shows Count have '{age_group_data_tvshows['TV Shows Count'].max()}' TV Shows Available is '{df_age_group_high_tvshows['Age Group'][0]}', &\n
The Age Group with Lowest TV Shows Count have '{age_group_data_tvshows['TV Shows Count'].min()}' TV Shows Available is '{df_age_group_low_tvshows['Age Group'][0]}'
''')
Total '3507' Titles are available on All Platforms, out of which
You Can Choose to see TV Shows from Total '5' Age Group, They were Like this,
[16 7 18 0 13] etc.
The Age Group with Highest TV Shows Count have '1018' TV Shows Available is '16', &
The Age Group with Lowest TV Shows Count have '64' TV Shows Available is '13'
netflix_age_group_tvshows = age_group_data_tvshows[age_group_data_tvshows['Netflix'] != 0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_age_group_tvshows = netflix_age_group_tvshows.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
netflix_age_group_high_tvshows = df_age_group_high_tvshows.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_age_group_high_tvshows = netflix_age_group_high_tvshows.drop(['index'], axis = 1)
netflix_age_group_low_tvshows = df_age_group_high_tvshows.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_age_group_low_tvshows = netflix_age_group_low_tvshows.drop(['index'], axis = 1)
netflix_age_group_high_tvshows.head(5)
| Age Group | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 18 | 946 | 497 | 233 | 253 | 1 |
| 1 | 16 | 1018 | 350 | 521 | 236 | 7 |
| 2 | 7 | 958 | 334 | 372 | 306 | 72 |
| 3 | 0 | 521 | 147 | 150 | 207 | 73 |
| 4 | 13 | 64 | 4 | 9 | 26 | 30 |
hulu_age_group_tvshows = age_group_data_tvshows[age_group_data_tvshows['Hulu'] != 0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_age_group_tvshows = hulu_age_group_tvshows.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
hulu_age_group_high_tvshows = df_age_group_high_tvshows.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_age_group_high_tvshows = hulu_age_group_high_tvshows.drop(['index'], axis = 1)
hulu_age_group_low_tvshows = df_age_group_high_tvshows.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_age_group_low_tvshows = hulu_age_group_low_tvshows.drop(['index'], axis = 1)
hulu_age_group_high_tvshows.head(5)
| Age Group | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 16 | 1018 | 350 | 521 | 236 | 7 |
| 1 | 7 | 958 | 334 | 372 | 306 | 72 |
| 2 | 18 | 946 | 497 | 233 | 253 | 1 |
| 3 | 0 | 521 | 147 | 150 | 207 | 73 |
| 4 | 13 | 64 | 4 | 9 | 26 | 30 |
prime_video_age_group_tvshows = age_group_data_tvshows[age_group_data_tvshows['Prime Video'] != 0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_age_group_tvshows = prime_video_age_group_tvshows.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'TV Shows Count'], axis = 1)
prime_video_age_group_high_tvshows = df_age_group_high_tvshows.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_age_group_high_tvshows = prime_video_age_group_high_tvshows.drop(['index'], axis = 1)
prime_video_age_group_low_tvshows = df_age_group_high_tvshows.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_age_group_low_tvshows = prime_video_age_group_low_tvshows.drop(['index'], axis = 1)
prime_video_age_group_high_tvshows.head(5)
| Age Group | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 7 | 958 | 334 | 372 | 306 | 72 |
| 1 | 18 | 946 | 497 | 233 | 253 | 1 |
| 2 | 16 | 1018 | 350 | 521 | 236 | 7 |
| 3 | 0 | 521 | 147 | 150 | 207 | 73 |
| 4 | 13 | 64 | 4 | 9 | 26 | 30 |
disney_age_group_tvshows = age_group_data_tvshows[age_group_data_tvshows['Disney+'] != 0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_age_group_tvshows = disney_age_group_tvshows.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'TV Shows Count'], axis = 1)
disney_age_group_high_tvshows = df_age_group_high_tvshows.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_age_group_high_tvshows = disney_age_group_high_tvshows.drop(['index'], axis = 1)
disney_age_group_low_tvshows = df_age_group_high_tvshows.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_age_group_low_tvshows = disney_age_group_low_tvshows.drop(['index'], axis = 1)
disney_age_group_high_tvshows.head(5)
| Age Group | TV Shows Count | Netflix | Hulu | Prime Video | Disney+ | |
|---|---|---|---|---|---|---|
| 0 | 0 | 521 | 147 | 150 | 207 | 73 |
| 1 | 7 | 958 | 334 | 372 | 306 | 72 |
| 2 | 13 | 64 | 4 | 9 | 26 | 30 |
| 3 | 16 | 1018 | 350 | 521 | 236 | 7 |
| 4 | 18 | 946 | 497 | 233 | 253 | 1 |
print(f'''
The Age Group with Highest TV Shows Count Ever Got is '{df_age_group_high_tvshows['Age Group'][0]}' : '{df_age_group_high_tvshows['TV Shows Count'].max()}'\n
The Age Group with Lowest TV Shows Count Ever Got is '{df_age_group_low_tvshows['Age Group'][0]}' : '{df_age_group_low_tvshows['TV Shows Count'].min()}'\n
The Age Group with Highest TV Shows Count on 'Netflix' is '{netflix_age_group_high_tvshows['Age Group'][0]}' : '{netflix_age_group_high_tvshows['Netflix'].max()}'\n
The Age Group with Lowest TV Shows Count on 'Netflix' is '{netflix_age_group_low_tvshows['Age Group'][0]}' : '{netflix_age_group_low_tvshows['Netflix'].min()}'\n
The Age Group with Highest TV Shows Count on 'Hulu' is '{hulu_age_group_high_tvshows['Age Group'][0]}' : '{hulu_age_group_high_tvshows['Hulu'].max()}'\n
The Age Group with Lowest TV Shows Count on 'Hulu' is '{hulu_age_group_low_tvshows['Age Group'][0]}' : '{hulu_age_group_low_tvshows['Hulu'].min()}'\n
The Age Group with Highest TV Shows Count on 'Prime Video' is '{prime_video_age_group_high_tvshows['Age Group'][0]}' : '{prime_video_age_group_high_tvshows['Prime Video'].max()}'\n
The Age Group with Lowest TV Shows Count on 'Prime Video' is '{prime_video_age_group_low_tvshows['Age Group'][0]}' : '{prime_video_age_group_low_tvshows['Prime Video'].min()}'\n
The Age Group with Highest TV Shows Count on 'Disney+' is '{disney_age_group_high_tvshows['Age Group'][0]}' : '{disney_age_group_high_tvshows['Disney+'].max()}'\n
The Age Group with Lowest TV Shows Count on 'Disney+' is '{disney_age_group_low_tvshows['Age Group'][0]}' : '{disney_age_group_low_tvshows['Disney+'].min()}'\n
''')
The Age Group with Highest TV Shows Count Ever Got is '16' : '1018'
The Age Group with Lowest TV Shows Count Ever Got is '13' : '64'
The Age Group with Highest TV Shows Count on 'Netflix' is '18' : '497'
The Age Group with Lowest TV Shows Count on 'Netflix' is '13' : '4'
The Age Group with Highest TV Shows Count on 'Hulu' is '16' : '521'
The Age Group with Lowest TV Shows Count on 'Hulu' is '13' : '9'
The Age Group with Highest TV Shows Count on 'Prime Video' is '7' : '306'
The Age Group with Lowest TV Shows Count on 'Prime Video' is '13' : '26'
The Age Group with Highest TV Shows Count on 'Disney+' is '0' : '73'
The Age Group with Lowest TV Shows Count on 'Disney+' is '18' : '1'
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_a_ax1 = sns.barplot(x = netflix_age_group_tvshows['Age Group'], y = netflix_age_group_tvshows['Netflix'], palette = 'Reds_r', ax = axes[0, 0])
h_a_ax2 = sns.barplot(x = hulu_age_group_tvshows['Age Group'], y = hulu_age_group_tvshows['Hulu'], palette = 'Greens_r', ax = axes[0, 1])
p_a_ax3 = sns.barplot(x = prime_video_age_group_tvshows['Age Group'], y = prime_video_age_group_tvshows['Prime Video'], palette = 'Blues_r', ax = axes[1, 0])
d_a_ax4 = sns.barplot(x = disney_age_group_tvshows['Age Group'], y = disney_age_group_tvshows['Disney+'], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_a_ax1.title.set_text(labels[0])
h_a_ax2.title.set_text(labels[1])
p_a_ax3.title.set_text(labels[2])
d_a_ax4.title.set_text(labels[3])
plt.show()
plt.figure(figsize = (20, 5))
sns.lineplot(x = age_group_data_tvshows['Age Group'], y = age_group_data_tvshows['Netflix'], color = 'red')
sns.lineplot(x = age_group_data_tvshows['Age Group'], y = age_group_data_tvshows['Hulu'], color = 'lightgreen')
sns.lineplot(x = age_group_data_tvshows['Age Group'], y = age_group_data_tvshows['Prime Video'], color = 'lightblue')
sns.lineplot(x = age_group_data_tvshows['Age Group'], y = age_group_data_tvshows['Disney+'], color = 'darkblue')
plt.xlabel('Age Group', fontsize = 15)
plt.ylabel('TV Shows Count', fontsize = 15)
plt.show()
print(f'''
Accross All Platforms Total Count of Age Group is '{age_group_data_tvshows['Age Group'].unique().shape[0]}'\n
Total Count of Age Group on 'Netflix' is '{netflix_age_group_tvshows['Age Group'].unique().shape[0]}'\n
Total Count of Age Group on 'Hulu' is '{hulu_age_group_tvshows['Age Group'].unique().shape[0]}'\n
Total Count of Age Group on 'Prime Video' is '{prime_video_age_group_tvshows['Age Group'].unique().shape[0]}'\n
Total Count of Age Group on 'Disney+' is '{disney_age_group_tvshows['Age Group'].unique().shape[0]}'\n
''')
Accross All Platforms Total Count of Age Group is '5'
Total Count of Age Group on 'Netflix' is '5'
Total Count of Age Group on 'Hulu' is '5'
Total Count of Age Group on 'Prime Video' is '5'
Total Count of Age Group on 'Disney+' is '5'
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_a_ax1 = sns.lineplot(y = age_group_data_tvshows['Age Group'], x = age_group_data_tvshows['Netflix'], color = 'red', ax = axes[0, 0])
h_a_ax2 = sns.lineplot(y = age_group_data_tvshows['Age Group'], x = age_group_data_tvshows['Hulu'], color = 'lightgreen', ax = axes[0, 1])
p_a_ax3 = sns.lineplot(y = age_group_data_tvshows['Age Group'], x = age_group_data_tvshows['Prime Video'], color = 'lightblue', ax = axes[1, 0])
d_a_ax4 = sns.lineplot(y = age_group_data_tvshows['Age Group'], x = age_group_data_tvshows['Disney+'], color = 'darkblue', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_a_ax1.title.set_text(labels[0])
h_a_ax2.title.set_text(labels[1])
p_a_ax3.title.set_text(labels[2])
d_a_ax4.title.set_text(labels[3])
plt.show()
fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
n_a_ax1 = sns.barplot(x = age_group_data_tvshows['Age Group'], y = age_group_data_tvshows['Netflix'], palette = 'Reds_r', ax = axes[0, 0])
h_a_ax2 = sns.barplot(x = age_group_data_tvshows['Age Group'], y = age_group_data_tvshows['Hulu'], palette = 'Greens_r', ax = axes[0, 1])
p_a_ax3 = sns.barplot(x = age_group_data_tvshows['Age Group'], y = age_group_data_tvshows['Prime Video'], palette = 'Blues_r', ax = axes[1, 0])
d_a_ax4 = sns.barplot(x = age_group_data_tvshows['Age Group'], y = age_group_data_tvshows['Disney+'], palette = 'BuPu_r', ax = axes[1, 1])
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
n_a_ax1.title.set_text(labels[0])
h_a_ax2.title.set_text(labels[1])
p_a_ax3.title.set_text(labels[2])
d_a_ax4.title.set_text(labels[3])
plt.show()